Regression-based surface water fraction mapping using a synthetic spectral library for monitoring small water bodies

ABSTRACT

Small water bodies (SWBs), such as ponds and on-farm reservoirs, are a key part of the hydrological system and play important roles in diverse domains from agriculture to conservation. The monitoring of SWBs has been greatly facilitated by medium-spatial-resolution satellite images, but the monitoring accuracy is considerably affected by the mixed-pixel problem. Although various spectral unmixing methods have been applied to map sub-pixel surface water fractions for large water bodies, such as lakes and reservoirs, it is challenging to map SWBs that are small in size relative to the image pixel and have dissimilar spectral properties. In this study, a novel regression-based surface water fraction mapping method (RSWFM) using a random forest and a synthetic spectral library is proposed for mapping 10 m spatial resolution surface water fractions from Sentinel-2 imagery. The RSWFM inputs a few endmembers of water, vegetation, impervious surfaces, and soil to simulate a spectral library, and considers spectral variations in endmembers for different SWBs. Additionally, RSWFM applies noise-based data augmentation on pure endmembers to overcome the limitation often arising from the use of a small set of pure spectra in training the regression model. RSWFM was assessed in ten study sites and compared with the fully constrained least squares (FCLS) linear spectral mixture analysis, multiple endmember spectral mixture analysis (MESMA), and the nonlinear random forest (RF) regression without data-augmentation. The results showed that RSWFM decreases the water fraction mapping errors by ~ 30%, ~15%, and ~ 11% in root mean square error compared with the linear FCLS, MESMA unmixings, and the nonlinear RF regression without data-augmentation respectively. RSWFM has an accuracy of approximately 0.85 in R² in estimating the area of SWBs smaller than 1 ha.

KEYWORDS:

• spectral unmixing
• small water body
• Sentinel-2
• random forest
• synthetic spectral library

Acknowledgments

The authors would like to thank Yunning Peng, Xuliang Xiang, and Jiayuan Duan for manually interpreting the small water bodies from Google Earth images used for validation.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available upon request from the corresponding author (Xiaodong Li, [email protected]). The codes of RSWFM and RSWFM v1.0 are available online for mapping sub-pixel surface water fractions of SWBs from Sentinel-2 images (https://github.com/PolarLan/RSWFM.git).

Additional information

Funding

This work was supported by the Natural Science Foundation of China [62071457]; the Natural Science Foundation of China [42271400]; the International Science and Technology Cooperation Project from Hubei Province, China [2022EHB018]; Science and Technology Partnership Program, Ministry of Science and Technology of China [KY201802007], Key Research Program of Frontier Sciences, Chinese Academy of Sciences [ZDBS-LY-DQC034]; Young Top-notch Talent Cultivation Program of Hubei Province, Application Foundation Frontier Project of Wuhan [2020020601012283]; and the Hubei Provincial Natural Science Foundation of China for Distinguished Young Scholars [2022CFA045].